基于应变模态和稀疏贝叶斯学习的网架结构损伤识别

传统稀疏贝叶斯学习算法进行损伤识别时需要对每个单元进行刚度损伤系数的迭代更新,当结构单元众多时,存在计算效率低和对振型的完备性要求高等问题.本文提出了损伤识别两步法,首先利用应变模态差指标进行疑似损伤单元的判断;接着以单元刚度损伤系数为目标参数,建立结构损伤识别的多层次稀疏贝叶斯学习模型,利用稀疏贝叶斯学习算法进一步识...

Damage identification of truss structure based on strain mode and sparse Bayesian learning

When the traditional sparse Bayesian learning algorithm is adopted for structure damage identification, the stiffness damage parameters of each element need to be updated iteratively.The problems such as low computational efficiency and high requirements for completeness of mode shapes will be very serious when there are many structural elements.In order to solve the above problems, a two-step method for damage identification is proposed.Firstly, the modal strain difference index is used to judge the suspected damage elements.Secondly, the element stiffness damage parameters are taken as the target to establish a multi-level sparse Bayesian learning model for structural damage identification.Then the damage location and severity of suspected damage elements are identified using the sparse Bayesian learning algorithm.A space truss structure is used to verify object, the effectiveness of the proposed method for identification of single damage and multi-damage.